{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sequential Attend, Infer, Repeat (SQAIR)\n",
    "This notebook loads a pre-trained model checkpoints, evaluates it on the validation dataset and plots reconstructions with attention bounding-boxes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### General Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "from sqair.experiment_tools import (load, init_checkpoint, parse_flags, get_session, print_flags,\n",
    "                                      print_num_params, print_variables_by_scope)\n",
    "from sqair import tf_flags as flags\n",
    "from sqair.eval_tools import bbox_colors, make_expr_logger, rect_stn\n",
    "from sqair.modules import SpatialTransformer as ST\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model config\n",
    "This notebook loads a pre-trained model checkpoint by default. To change which checkpoint is loaded, change the `checkpoint_iter` variable below. \n",
    "\n",
    "You can run `scripts/download_models.sh` to download checkpoints saved after `int(i * 1e5) for i in [1, ..., 10]` training iterations.\n",
    "\n",
    "Note that the model is trained with a curriculum of sequences of increasing length. After the model is resumed, you can query the starting sequence length by printing `F.seq_len` and the number of iterations it is trained with this sequence length by printing `F.stage_itr`. The maximum sequence length is 10 time-steps. If you load any checkpoint but the last one, the longest sequence the model has seen until this point might be smaller than 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "F = flags.FLAGS\n",
    "F.data_config = '../sqair/configs/seq_mnist_data.py'\n",
    "F.model_config = '../sqair/configs/mlp_mnist_model.py'\n",
    "F.batch_size = 32\n",
    "F.debug = False\n",
    "\n",
    "# Length of iamge sequences\n",
    "sequence_length = 10\n",
    "\n",
    "# The model will be evaluated on this many batches from the validation set. \n",
    "# Set this -1 to evaluate on the whole validation set.\n",
    "num_eval_batches = 2  \n",
    "\n",
    "checkpoint_iter = int(10e5)\n",
    "checkpoint_dir = '../release_models/mnist_mlp/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading flags from ../sqair/configs/mlp_mnist_model.py\n",
      "loading flags from ../sqair/configs/seq_mnist_data.py\n"
     ]
    }
   ],
   "source": [
    "resume_checkpoint = '1/model.ckpt-{}'.format(checkpoint_iter)\n",
    "resume_checkpoint = os.path.join(checkpoint_dir, resume_checkpoint)\n",
    "logdir, flags, _ = init_checkpoint(checkpoint_dir, F.data_config, F.model_config, True)\n",
    "F.seq_len = sequence_length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cwd = os.getcwd()\n",
    "os.chdir('../sqair')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 'seq_mnist_data' from ../sqair/configs/seq_mnist_data.pyc\n",
      "Loading 'mlp_mnist_model' from ../sqair/configs/mlp_mnist_model.pyc\n",
      "WARNING:tensorflow:Variable **= will be deprecated. Use 'x = x ** y' if you want a new python Tensor object.\n",
      "WARNING:tensorflow:From ../sqair/sqair_modules.py:484: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:Variable += will be deprecated. Use variable.assign_add if you want assignment to the variable value or 'x = x + y' if you want a new python Tensor object.\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "data_dict = load(F.data_config, F.batch_size)\n",
    "\n",
    "# mean img\n",
    "imgs = data_dict.train_data.imgs\n",
    "mean_img = imgs.mean(tuple(range(len(imgs.shape) - 2)))\n",
    "assert len(mean_img.shape) == 2\n",
    "\n",
    "try:\n",
    "    coords = data_dict.train_coord\n",
    "except AttributeError:\n",
    "    coords = None\n",
    "\n",
    "model = load(F.model_config, img=data_dict.train_img, coords=coords, num=data_dict.train_num,\n",
    "             mean_img=mean_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir(cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flags:\n",
      "============================================================\n",
      "\tbatch_size: 32\n",
      "\tconstant_prop_prior: 0.0\n",
      "\tdata_config: configs/seq_mnist_data.py\n",
      "\tdebug: False\n",
      "\tdisc_prior_type: cat\n",
      "\tdisc_step_bias: 1.0\n",
      "\teval_on_train: True\n",
      "\teval_size_fraction: 0.1\n",
      "\tfig_itr: 20000\n",
      "\tgit_commit: 48f6d55643a570471da9275ef780a7421b583562\n",
      "\tglimpse_size: 20\n",
      "\tgpu: 0\n",
      "\tinput_type: normal\n",
      "\tk_particles: 5\n",
      "\tl2: 0.0\n",
      "\tlearning_rate: 1e-05\n",
      "\tlog_at_start: False\n",
      "\tlog_itr: 10000\n",
      "\tmasked_glimpse: True\n",
      "\tmodel_config: configs/mlp_mnist_model.py\n",
      "\tn_steps_per_image: 3\n",
      "\tn_units: 8\n",
      "\tn_what: 50\n",
      "\topt: rmsprop\n",
      "\toutput_scale: 0.25\n",
      "\toutput_std: 0.3\n",
      "\tper_timestep_vimco: False\n",
      "\tprior_transition: GRU\n",
      "\tprop_prior_step_bias: 10.0\n",
      "\tprop_prior_type: rnn\n",
      "\tprop_step_bias: 5.0\n",
      "\trec_where_prior: True\n",
      "\treport_loss_every: 1000\n",
      "\tresults_dir: ../checkpoints\n",
      "\tresume: False\n",
      "\trun_name: mnit_mlp\n",
      "\tsample_from_prior: False\n",
      "\tsave_itr: 100000\n",
      "\tscale_prior: -2.0\n",
      "\tschedule: 4,6,10\n",
      "\tseq_len: 10\n",
      "\tstage_itr: 200000\n",
      "\tstep_success_prob: 0.75\n",
      "\ttest_run: False\n",
      "\ttime_transition: GRU\n",
      "\ttrain_itr: 2000000\n",
      "\ttrain_path: seq_mnist_train.pickle\n",
      "\ttransform_var_bias: -3.0\n",
      "\ttransition: VanillaRNN\n",
      "\tvalid_path: seq_mnist_validation.pickle\n",
      "============================================================\n",
      "\n",
      "\n",
      "Trainable Variables:\n",
      "scope: decoder\n",
      "\tdecoder/air_decoder/Variable:0 [50, 50, 1]\n",
      "\tdecoder/air_decoder/decoder/mlp/linear/b:0 [256]\n",
      "\tdecoder/air_decoder/decoder/mlp/linear/w:0 [50, 256]\n",
      "\tdecoder/air_decoder/decoder/mlp/linear_1/b:0 [256]\n",
      "\tdecoder/air_decoder/decoder/mlp/linear_1/w:0 [256, 256]\n",
      "\tdecoder/air_decoder/decoder/mlp/linear_2/b:0 [400]\n",
      "\tdecoder/air_decoder/decoder/mlp/linear_2/w:0 [256, 400]\n",
      "\tdecoder/air_decoder/decoder/output_scale:0 []\n",
      "decoder scope params = 184 149\n",
      "\n",
      "scope: discovery\n",
      "\tdiscovery/discover/discovery/vanilla_rnn_initial_state_0/w:0 [1, 256]\n",
      "\tdiscovery/discover/mlp/linear/b:0 [10]\n",
      "\tdiscovery/discover/mlp/linear/w:0 [1, 10]\n",
      "\tdiscovery/discover/mlp/linear_1/b:0 [4]\n",
      "\tdiscovery/discover/mlp/linear_1/w:0 [10, 4]\n",
      "\tdiscovery/discover/recurrent_normal_impl/discovery/discover/recurrent_normal_impl/vanilla_rnn_initial_state_0/w:0 [1, 4]\n",
      "\tdiscovery/discover/recurrent_normal_impl/init_sample:0 [1, 4]\n",
      "\tdiscovery/discover/recurrent_normal_impl/linear/b:0 [8]\n",
      "\tdiscovery/discover/recurrent_normal_impl/linear/w:0 [4, 8]\n",
      "\tdiscovery/discover/recurrent_normal_impl/linear_1/b:0 [128]\n",
      "\tdiscovery/discover/recurrent_normal_impl/linear_1/w:0 [261, 128]\n",
      "\tdiscovery/discover/recurrent_normal_impl/vanilla_rnn/hidden_to_hidden/b:0 [4]\n",
      "\tdiscovery/discover/recurrent_normal_impl/vanilla_rnn/hidden_to_hidden/w:0 [128, 4]\n",
      "\tdiscovery/discover/recurrent_normal_impl/vanilla_rnn/in_to_hidden/b:0 [4]\n",
      "\tdiscovery/discover/recurrent_normal_impl/vanilla_rnn/in_to_hidden/w:0 [4, 4]\n",
      "\tdiscovery/discovery_core/air_encoder/gaussian_from_param_vec/linear/b:0 [100]\n",
      "\tdiscovery/discovery_core/air_encoder/gaussian_from_param_vec/linear/w:0 [256, 100]\n",
      "\tdiscovery/discovery_core/air_encoder/mlp/linear/b:0 [128]\n",
      "\tdiscovery/discovery_core/air_encoder/mlp/linear/w:0 [256, 128]\n",
      "\tdiscovery/discovery_core/air_encoder/mlp/linear_1/b:0 [400]\n",
      "\tdiscovery/discovery_core/air_encoder/mlp/linear_1/w:0 [128, 400]\n",
      "\tdiscovery/discovery_core/encoder/mlp/linear/b:0 [256]\n",
      "\tdiscovery/discovery_core/encoder/mlp/linear/w:0 [2500, 256]\n",
      "\tdiscovery/discovery_core/encoder/mlp/linear_1/b:0 [256]\n",
      "\tdiscovery/discovery_core/encoder/mlp/linear_1/w:0 [256, 256]\n",
      "\tdiscovery/discovery_core/encoder_1/mlp/linear/b:0 [256]\n",
      "\tdiscovery/discovery_core/encoder_1/mlp/linear/w:0 [400, 256]\n",
      "\tdiscovery/discovery_core/encoder_1/mlp/linear_1/b:0 [256]\n",
      "\tdiscovery/discovery_core/encoder_1/mlp/linear_1/w:0 [256, 256]\n",
      "\tdiscovery/discovery_core/steps_predictor/mlp/linear/b:0 [128]\n",
      "\tdiscovery/discovery_core/steps_predictor/mlp/linear/w:0 [306, 128]\n",
      "\tdiscovery/discovery_core/steps_predictor/mlp/linear_1/b:0 [1]\n",
      "\tdiscovery/discovery_core/steps_predictor/mlp/linear_1/w:0 [128, 1]\n",
      "\tdiscovery/discovery_core/stochastic_transform_param/mlp/linear/b:0 [256]\n",
      "\tdiscovery/discovery_core/stochastic_transform_param/mlp/linear/w:0 [256, 256]\n",
      "\tdiscovery/discovery_core/stochastic_transform_param/mlp/linear_1/b:0 [256]\n",
      "\tdiscovery/discovery_core/stochastic_transform_param/mlp/linear_1/w:0 [256, 256]\n",
      "\tdiscovery/discovery_core/stochastic_transform_param/mlp/linear_2/b:0 [8]\n",
      "\tdiscovery/discovery_core/stochastic_transform_param/mlp/linear_2/w:0 [256, 8]\n",
      "\tdiscovery/discovery_core/stochastic_transform_param/scale_offset:0 []\n",
      "\tdiscovery/vanilla_rnn/hidden_to_hidden/b:0 [256]\n",
      "\tdiscovery/vanilla_rnn/hidden_to_hidden/w:0 [256, 256]\n",
      "\tdiscovery/vanilla_rnn/in_to_hidden/b:0 [256]\n",
      "\tdiscovery/vanilla_rnn/in_to_hidden/w:0 [567, 256]\n",
      "discovery scope params = 1 403 398\n",
      "\n",
      "scope: model\n",
      "\tmodel/sequential_air/while/sqair_timestep/discover/step_prior_bias:0 [4]\n",
      "\tmodel/sequential_air/while/sqair_timestep/discover/step_prior_timestep_bias:0 [4]\n",
      "model scope params = 8\n",
      "\n",
      "scope: propagation\n",
      "\tpropagation/gru/bh:0 [256]\n",
      "\tpropagation/gru/br:0 [256]\n",
      "\tpropagation/gru/bz:0 [256]\n",
      "\tpropagation/gru/uh:0 [256, 256]\n",
      "\tpropagation/gru/ur:0 [256, 256]\n",
      "\tpropagation/gru/uz:0 [256, 256]\n",
      "\tpropagation/gru/wh:0 [360, 256]\n",
      "\tpropagation/gru/wr:0 [360, 256]\n",
      "\tpropagation/gru/wz:0 [360, 256]\n",
      "\tpropagation/gru_1/bh:0 [256]\n",
      "\tpropagation/gru_1/br:0 [256]\n",
      "\tpropagation/gru_1/bz:0 [256]\n",
      "\tpropagation/gru_1/uh:0 [256, 256]\n",
      "\tpropagation/gru_1/ur:0 [256, 256]\n",
      "\tpropagation/gru_1/uz:0 [256, 256]\n",
      "\tpropagation/gru_1/wh:0 [54, 256]\n",
      "\tpropagation/gru_1/wr:0 [54, 256]\n",
      "\tpropagation/gru_1/wz:0 [54, 256]\n",
      "\tpropagation/propagate_prior/linear/b:0 [109]\n",
      "\tpropagation/propagate_prior/linear/w:0 [256, 109]\n",
      "\tpropagation/propagation_core/affine_diag_normal/cholesky_scale:0 [10]\n",
      "\tpropagation/propagation_core/rnn_inpt/mlp/linear/b:0 [128]\n",
      "\tpropagation/propagation_core/rnn_inpt/mlp/linear/w:0 [256, 128]\n",
      "\tpropagation/propagation_core/rnn_inpt/mlp/linear_1/b:0 [4]\n",
      "\tpropagation/propagation_core/rnn_inpt/mlp/linear_1/w:0 [128, 4]\n",
      "\tpropagation/propagation_core/steps_predictor/mlp/linear/b:0 [128]\n",
      "\tpropagation/propagation_core/steps_predictor/mlp/linear/w:0 [562, 128]\n",
      "\tpropagation/propagation_core/steps_predictor/mlp/linear_1/b:0 [1]\n",
      "\tpropagation/propagation_core/steps_predictor/mlp/linear_1/w:0 [128, 1]\n",
      "\tpropagation/propagation_core/stochastic_transform_param/mlp/linear/b:0 [256]\n",
      "\tpropagation/propagation_core/stochastic_transform_param/mlp/linear/w:0 [516, 256]\n",
      "\tpropagation/propagation_core/stochastic_transform_param/mlp/linear_1/b:0 [256]\n",
      "\tpropagation/propagation_core/stochastic_transform_param/mlp/linear_1/w:0 [256, 256]\n",
      "\tpropagation/propagation_core/stochastic_transform_param/mlp/linear_2/b:0 [8]\n",
      "\tpropagation/propagation_core/stochastic_transform_param/mlp/linear_2/w:0 [256, 8]\n",
      "\tpropagation/propagation_core/stochastic_transform_param/scale_offset:0 []\n",
      "\tpropagation/propagation_core/what/gaussian_from_param_vec/linear/b:0 [100]\n",
      "\tpropagation/propagation_core/what/gaussian_from_param_vec/linear/w:0 [256, 100]\n",
      "\tpropagation/propagation_core/what/linear/b:0 [150]\n",
      "\tpropagation/propagation_core/what/linear/w:0 [256, 150]\n",
      "\tpropagation/sequential_ssm/propagation/vanilla_rnn_initial_state_0/w:0 [1, 256]\n",
      "\tpropagation/vanilla_rnn/hidden_to_hidden/b:0 [256]\n",
      "\tpropagation/vanilla_rnn/hidden_to_hidden/w:0 [256, 256]\n",
      "\tpropagation/vanilla_rnn/in_to_hidden/b:0 [256]\n",
      "\tpropagation/vanilla_rnn/in_to_hidden/w:0 [416, 256]\n",
      "propagation scope params = 1 283 583\n",
      "\n",
      "scope: sequence\n",
      "\tsequence/sequential_air/propagation/gru_1_initial_state_0/w:0 [1, 256]\n",
      "\tsequence/sequential_air/propagation/gru_initial_state_0/w:0 [1, 256]\n",
      "\tsequence/sequential_air/sqair_timestep/mlp/linear/b:0 [256]\n",
      "\tsequence/sequential_air/sqair_timestep/mlp/linear/w:0 [54, 256]\n",
      "\tsequence/sequential_air/sqair_timestep/mlp/linear_1/b:0 [256]\n",
      "sequence scope params = 14 848\n",
      "\n",
      "scope: sequence\n",
      "\tsequence/sequential_air/sqair_timestep/mlp/linear_1/w:0 [256, 256]\n",
      "\n",
      "Number of trainable parameters: 2 951 522\n"
     ]
    }
   ],
   "source": [
    "print_flags()\n",
    "print '\\nTrainable Variables:'\n",
    "print_variables_by_scope()\n",
    "print_num_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saver = tf.train.Saver()\n",
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ../release_models/mnist_mlp/1/model.ckpt-1000000\n"
     ]
    }
   ],
   "source": [
    "sess.run(tf.global_variables_initializer())\n",
    "saver.restore(sess, resume_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequence length: 10\n"
     ]
    }
   ],
   "source": [
    "def set_sequence_length(sess, seq_len):\n",
    "    global_step = tf.train.get_or_create_global_step()\n",
    "    target_step = F.stage_itr * (seq_len - F.seq_len) + 1\n",
    "    sess.run(global_step.assign(target_step))\n",
    "    print 'sequence length:', sess.run(model.n_timesteps)\n",
    "    \n",
    "set_sequence_length(sess, sequence_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the loaded model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create an eval function\n",
    "\n",
    "if num_eval_batches == -1:\n",
    "    num_eval_batches = data_dict.valid_data.imgs.shape[1] // F.batch_size\n",
    "\n",
    "exprs = {\n",
    "    'elbo_vae': model.elbo_vae,\n",
    "    'elbo_iwae': model.elbo_iwae,\n",
    "    'num_steps/t': model.num_steps,\n",
    "    'num_disc_steps/t': model.num_disc_steps,\n",
    "    'num_prop_steps/t': model.num_prop_steps,\n",
    "    'num_steps_acc': model.num_step_accuracy,\n",
    "    'data_ll': model.data_ll,\n",
    "    'log_p_z': model.log_p_z,\n",
    "    'log_q_z_given_x': model.log_q_z_given_x,\n",
    "    'kl': model.kl,\n",
    "}\n",
    "    \n",
    "train_tensors = data_dict.train_tensors\n",
    "valid_tensors = data_dict.valid_tensors\n",
    "valid_fd = {train_tensors[k]: valid_tensors[k] for k in train_tensors}\n",
    "test_log = make_expr_logger(sess, num_eval_batches, exprs, name='validation', data_dict=valid_fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1000000, Data validation data_ll = 640.4481, elbo_iwae = 6095.4565, elbo_vae = 5941.5574, kl = 30.7866, log_p_z = 16.1933, log_q_z_given_x = 46.9800, num_disc_steps/t = 0.1678, num_prop_steps/t = 0.9275, num_steps/t = 1.0953, num_steps_acc = 0.9453, eval time = 1.871s\n"
     ]
    }
   ],
   "source": [
    "# evalaute the model\n",
    "_ = test_log(checkpoint_iter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's plot some reconstructions & attention glimpses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def display_seq(imgs, recs, where, pres, obj_id=None):\n",
    "    \n",
    "    n_timesteps = imgs.shape[0]\n",
    "    h, w = imgs.shape[1:3]\n",
    "\n",
    "    if obj_id is not None:\n",
    "        unique_ids = np.unique(obj_id)[1:] # remove id == -1\n",
    "        color_by_id = {i: c for i, c in zip(unique_ids, itertools.cycle(bbox_colors))}\n",
    "        color_by_id[-1] = 'k'\n",
    "        \n",
    "        \n",
    "        fig_size = 2 * np.asarray([n_timesteps, 2])\n",
    "        fig, axes = plt.subplots(2, n_timesteps, figsize=fig_size, sharex=True, sharey=True)\n",
    "\n",
    "        for t in xrange(n_timesteps):\n",
    "            axes[0, t].imshow(imgs[t], cmap='gray', vmin=0, vmax=1)\n",
    "            axes[1, t].imshow(recs[t], cmap='gray', vmin=0, vmax=1)\n",
    "            \n",
    "            for j in xrange(pres.shape[-1]):\n",
    "                if pres[t, j] == 1:\n",
    "\n",
    "                    color = color_by_id[obj_id[t, j]]\n",
    "                    bbox =  where[t, j]\n",
    "                    rect_stn(axes[1, t], w, h, bbox, color, line_width=3)\n",
    "\n",
    "                axes[0, t].set_xticks([])\n",
    "                axes[1, t].set_yticks([])\n",
    "        \n",
    "        axes[0, 0].set_ylabel('inputs', fontsize=14)\n",
    "        axes[1, 0].set_ylabel('recs', fontsize=14)\n",
    "                \n",
    "        plt.subplots_adjust(wspace=.05, hspace=.05)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# prepare data tensors\n",
    "# `resampled` means that each output of the model is resampled according\n",
    "# to importance weights (i.e. associated exp(vae elbo value), cf. IWAE)\n",
    "where = ST.to_coords(model.resampled_where)\n",
    "tensors = [model.resampled_canvas, where, model.resampled_presence, model.resampled_obj_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b676ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7l1qHrCRJ0rhZNJ0/zdRPesVuvPFGoHNlJ6hTO92q3peSHEkvaLfET1IJhx9+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\n5jezPl+Sn69//euBeoW4SN2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5D9327HTrrbfyve99D6hHmKGuF5LEQdIDeX+TQJhp1Dk1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\nJEmSJpidP5IkSZIkSRPs/weUh9nYgqWVdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127c88590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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www2BYh/GXXfd1bic+mxJ+7zyyivW/BkH5dbvqaFW/ezWrlZe6nFBUYsrn+tq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\nkiRJkqQB9v8D6PfVKPyiBR4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128bc8d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ARx55JJAmdSBNeYTRo0cDsGTJkrzlmzZtAmDUqFHZZTHzV9++fYvWdjW/q666CkhrN61Z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\nN26s1o761qZQ04rkQUz73bNnTwDGjBm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1297d57d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c69Fqbr31ViBVm0QVSmTNZEXlz4knngjAgQceCMCMGTOA9Ld3RqrK8rl8ZvLV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\nfcpCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c20c110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MOP/evXvzlkGSJEmSpNZi5I8kSZIkSVIFM/LnMHz44Yfx/ZAgOEQDheiPkPh5\n9OjRAAwaNKjReUKUydNPPw0kS7v/9a9/bYlitzsh4XKIwFm2bBmQRFgBHHvssQCMGTMm43FIzH3u\nuecCSRLnt956Kz724YcfBuC+++4D4N133814XUmSJBXuhbe20ffb84p+3rU/vLjo55SkUmfkjyRJ\nkiRJUgUz8ucQ1dTUZCwhXl1dnXO/kPPn6KOPznuuEF3y/PPPA/D4449nPH/kkUcCSQ6Z9Hwz6ffV\n2L59++jatSsAH3zwAZC8j7/4xS/i/cJzITfTJZdcAkBdXV3G+d5++20AHn300Xjb1KlTAXjllVeK\nXv5KEb4fIfoqV1RUiKoKn+l9+/YV7fXDucPrFvPckiRJklTq7PyRJEmSVHKG9e7C8mZM0QrpF9IH\nZiEZ/AHo8625xS2cJJUZO38OUXbkQIhoCFE8ffv2BWDcuHEADBgwIO+5OnXqBCSrfWXbuXMn0Dh3\njZpnx44dQNIwOOqoowB48cUX431ChNZZZ50FJKt6Be+88w4A8+Y1zDv/6U9/Gj9XX1+f83WtpwYt\nMVe/WJzzL0lSeaitrc14nN7RE6LkQ5s5PHewiGNJam/M+SNJkiRJklTBjPw5RKlUKiOUNET8BJ//\n/OeBZJWvlStXAvDmm28C8OlPfzreN4xWhH0O9poqTFVVVVw3IfKnQ4cOjfYLq3uF22zvvfceAI88\n8giQrBiWLoxIhXoKuWvMyyRJknR4Qs7GkEczPAZ4//33cx4TIn5si0mSnT+SWtG6H40HMufgh8Zb\n2Hb++ecD8LnPfQ5IpuB169YNSKbopYdwv/766wA8+OCDADz00ENA46mUVVVVzvmXJEmS1O7Y+XMY\nco0ifPnLXwbgH/7hH4Bkdahf/vKXQJJ/pn///vExYQRjy5YtLVfYdix0EoTOhW3btgHQvXv3eJ/L\nLrsMgEGDBuU8x9atWwFYsWJF3tcJ88vD6xip1VhY9St9dbzwPoUV1m677TYATj/9dCD5Dr322mtA\nkn/ppJNOis8RcmsNHjw44/V++9vfAsl3y5E/SZLKSxj0CSuz5ovySZe9yqckyZw/kiRJkiRJFc3I\nn8NwxBFHxPe/8pWvAHDLLbcASQ6gn/3sZwAsXLgQgCFDhmQ8D8lqUSG6JAg5ZLKnxRi90Hz79++P\n37fsvEwhSgtgzJgxQBJlEvIvhdW/wnseok9yCREs2SvBKRE+y+l1cfLJJwNw0003AUnEzxtvvAHA\n/fffD8Cf/vQnADZs2ADAZz7zmfgc3/zmNwHo3bs3AOeeey4Af/nLXwCj6iRJKlfHHXcckETP54qs\nztdGts0sSQkjfyRJkiRJkioLXdW6AAAd+klEQVSYnT+SJEmSJEkVzGlfh6hz585MmDAhfjxp0iQA\n+vXrB8B//Md/APCHP/wBSJLTnXrqqUBmwtulS5cCSThrEEJVTVZ3eLJDfr/whS8AcPnll8fbNm/e\nDMCCBQuAZFn4vn37Asl0pd27d7dsYStcrlDt8J0YOHBgxvawctfMmTMBeP755zOeP/HEE+P7ob7C\ndyhMHQvnfvXVV4HMZWElSVLpC23mDh06ALnbYqEdEKaVO91LUiVJ7w8I99O3NTftiJE/kiRJkiRJ\nFczIn0PUuXPneHlwSCIMwlLgf/vb34BklGLUqFEAXHjhhUCy3DjAM888A+RfKjz06jmKcWhCHYTl\nwL/1rW8B0LFjx3if3/zmNwA88sgjAJx55pkAvPfeewCsWbMm7/mzkwyG+nKp98ZCkvRdu3bF28L7\nFraFKLkQrbN27dqc5wrfKYCuXbsCyehgnz59AOjSpQuQ1HX4jkmSpPIQRrTDtTxX5I+LbUgqZ+Hv\n1SD8PRkWgAp/b6bvG26rq6vjRYuaYuePJEmSJElSKzrrv4YW5Txv/7OdPy1q586dGZEdIXohRC2c\nffbZAAwd2lChIZ/JgAEDAJgzZ0587LJlyzLOHXr2wihGek+fCnPEEUdw0UUXAfCd73wHSKJ6fvrT\nn8b7hfoIc8V79OgBwNatWwF46qmn8r5GiDbJjtQy8qex9IifIETjhOXYjznmGKAhug6grq4OSPL5\nDBo0CIDx48fH5wj7Bi+//DIAK1euBJJ6tU4kSSov4dodRrlz5cI0+lqSmtbsXoUoigZHUTQo7fGF\nURTdH0XRd6Ioqj7YsZIkSZIkSWobhUT+/CcwFXgliqI+wBzgz8DXgTrgO0UvXQnbtm1bnCcGoGfP\nngD06tULSHIAhVGK7du3A/DYY48B8Otf/zo+NnuOXr68JNm5ZdS0Y489liuvvBJIIn5efPFFAB59\n9NF4v1A/55xzDgAjRowAYP369UDj6Kww/xKSFaTMzXRo6uvrAXjllVeA5Ltz7rnnArB69Wogyf3z\n+c9/Hkii6iCpg/BdCiuEvfTSSxnPS5Kk8hKid7MjftJzZLgaq6Ry99erX2qU62fnzp1AZq7aTp06\nAUkO4T59+jR7dfBC5hN9DHj2wP0JwNOpVOrzwD8CVxRwHkmSJEmSJLWSQiJ/qoE9B+7/HfDggfur\ngOOLWahykR6988EHHwBw6aWXAkkEUIg4CJEjv/vd74Ak+uRQpPfsOaf54Gpra+M8SyEiJ+SDOemk\nk+L9Tj75ZADGjBkDJHllfvGLXwDw2muvAUl+H6N7Dk1NTcNPTvr7t2nTJgAefLDhJyV8d0L9hO/U\nhx9+CCSrfKXnwgr1M3v2bCD5bobV2oLm9opLkqTSEPJpBqEtlj4SbuSP2pvwPcgW2rrZbd70x9kr\nS6dSKaPkS8C+ffviiJ+Q+zfU89FHHx3vF/6eCjlPu3Tpwrvvvtus1ygk8udF4PooikbT0Pnz0IHt\nvYFNBZxHkiRJkiRJraSQyJ//C5gNTALuS6VSLxzYPg54ptgFKzd//OMfM26bkt772lSumNDz5woG\nhduwYQPz5s0D4PTTTwdgwoQJGbfpwshRyOc0a9YsoPGoUy7ZOZmsp8bCKF36Zz3MZX3ooYb+5NCL\nfcUVDbNJP/WpTwFJj3foAQ8RXJDkb5oxYwYAb7zxRsv8B8pQdrRacz6XLfFb4++XJOlQZOe8yF4V\nFxpHOXitUSUr1vLg6Z77pxVFP6cKlx2VFX7n0nOcZT/Xs2dP1qxZ06zzN7vzJ5VKLY6iqDtQl0ql\ntqQ99VPgw+aeR5LUek781zltXYS81v7w4rYugiRJktQuNLvzJ4qiE4CaVCq1LuupjwATaRQofUSi\nuaMTjmIUbvfu3dx7771Aklvmc5/7HJA5d3Lr1q0A/PnPfwZgzpyGP5hXrVoFFLbSWvZcW/MDJcLo\nXXrvdbdu3YCGKC2Ap59+GoALL7wQgJEjR2ac48033wRg8eLF8bYQqRVWDMvH75AkSeUljG5n58BI\nj/wJ20LbKzxnG0xqHvNitr2ampq4HsIqh0H6b1nYp66uDoDTTjuN5557rnmvUUB57gdmAj/P2n4R\nMBH4TAHnKnt9vz2vrYsgSZIkSWqnnv/nhoHP9IVQAD766KOM7ekDoKHz4Kijjsp4rMpXSOfPCODr\nObY/AfyoOMWRii9kP58+fToAv/rVr4DMHtSQ6yesKJWtOT+K2aNLRpk0Ft6j9N7s7NUKjj32WABO\nOOGEnOfYsWMHQEYP95NPPpmxz5FHHgkkFz5HABus/eHF8Wc5/X0P71MQVlT70pe+BMDAgQOBJFou\n1BEkKw7U19cDMHfuXAAWLlwIJJFagR3nktpC+AOokD9yzOFXWsJqROG6k14v4Tqf6w9dqZKF36lw\nm/1bF9rE4XsDycq6xxxzDADr1q2L29dqO9XV1XmvO+mPu3TpAiSzI8aOHdvsvMOFdP7UAB1zbD8i\nz3ZJkiSpzXQ4YQA9r5pa1HOar0ySVI4K6fx5Grj+wL90XweWFq1EJcyLvSRJkiRJKjeFdP5MBh6L\nouh04LED2y4AzgA+XeyCScUWwoXfe++9go9tznQhQ4ybFkJRjzjiiHhbCDMNoaeXXHIJ0JC8LJcw\nXenVV1/N+zq7du3KeGzdJLITkkNSH5/61KcAmDx5MgDnnXcekCTjXrGiYRnQjRs3xsf2798fgNGj\nRwPJ1LDNmzcD8NZbbwFOuZMkHZrsRTdCey47xwkk1/twjfP6r0qXPU0oe7pX+J6EdjYkbbbQ/tuz\nZ0+cJkNtK6TGCNP0Qv2mL1Q0YsQIAL7+9a/Hj++4445mnb+Qpd6fiqJoJPAt4LIDm58DvpZKpf7W\n3PNIrcn8IpIktV973l5ZcOR2+GMpNL737dvHif86p+hlkySpNRUS+cOBTp7/1UJlkVThQu91rsTa\nEyZMAJJkw6+//jqQRGqFUYqwTHxIMJyLI335ZScGBOjZsycA//iP/wgkEUChnn7/+98D8MADDwCw\ndevW+Njx48cD8OUvfxmAU045JeMczz//PJDUpyS1turqajp16gTAzp07gcxlwjt2bEhdGaJGw+9j\n+j5qO/kSn+ZavUhqb7ITPGcnPQ+33bt3j4856aSTANi+fTvQsOpX9gIsan25fsdC/aUvlrN+/Xog\n+Rtp06ZNjRZvyadxvOTBC3R8FEWToij6SRRFxx3Y9skoik4p5DySJEmSJElqHc2O/Imi6CzgUeB1\nYAhwD7AJuBAYCFzZEgWUCmVi7vJy8cUN9RUiSEJv9oMPPggkI7D9+vUDkmiULVu2tGo5K1lY8vMT\nn/hExvZ58xqmTf785z8HckdbhYisDz74AEiWgf/sZz8LwIIFC4CkXiWpte3bty/+jeratSuQmRsu\nX4SPucpKQ3bkT3YOIGhfOX5yRQdk5/PLld8Pcr8/4fOfb99c73d7eJ/LRXaunxABEnLEnH766QAM\nHz48PibUacif1aVLFyN/SkR2PQbpj0MEa2hb19fXNzunbSHTvu4BfpxKpb4bRdH7adsfBr5awHkk\nSZIkSQU467+GtnURMiy/6sW2LoKkAhTS+XMWcHWO7RuA44tTHEmVLORVuOyyy+JtN954I5CMKN1/\n//0APPZYw6KCYcQi5JlZuXIl0LhHHJKVwMzTkF9Y2SF91Ltz585Aktw0zCt+8cWGRt2qVasAqK2t\nBaBHjx7xsX379s04RxhpCnmEsueeS1JbCL9BIcfFwfIjNCdaQq0nXLd2796ddx/rSO1VaD+H363Q\nFg7tsBDxc/zxyZ/r3bp1A5L23I4dO+I2ntrOvn378kYxpj8+6qijAPjb3xrW3Fq0aFGzI38Kyfmz\nE+iaY/vHANeGkyRJkiRJKkGFRP7MAb4bRdHfH3iciqLoZOB/A78rcrkkVaALLrgAgK997WvxtjPP\nPBOAn/zkJwDMnTsXSEZlw4oEIRpl6dKlec+fPcfdkcDGwnuTPsIT3uv3338/Y9/wOHvUdcCAAfE+\nI0eOBJK8QSEC6OWXX864be4qBJJUbFEUxdeD5vwWGUVaWsIod6jDXBFA7bHOnv1qfd52TlgZNTsa\nJGyHhmgPSPK+NFepTT1r77IjFUM77IwzzgCSCPoTTjgh3idEBW3atAloyB1j5E/b++ijj+LvanbO\nufSI/fCbuHz5cgBeeumljNXADqaQyJ9JNET+bAQ6AUuAlcBW4NYCziNJkiRJkqRW0qzInyiKaoFH\ngK8AvYEzaeg4ejaVSv2p5YonqZJcdNFFAJx77rnxttdeew2Ap59+GkhGoM455xwAxowZAySrST33\n3HONzpu9EoURP/mFkYP092j16tUAvP766wCcdtppAAwbNgyAIUOGAMko4eWXXx4f+/GPfxyATp06\nAbBu3ToApk6dCsA777wDuGqOpLZRXV3NMccc0+x8COC1pNSEaK3siJ9w7Yf2FfHTHOGzGyIEwkqc\n6dEBIf9VU8zdV9pCxE743QqPQ16fEOWTHvkTPgdh5dx+/frFeTnVdlKpVLO+Z2GfsNrX0UcfHedG\nbUqzOn9SqdTeKIpOabibegx4rFlnlyRJkiRJUpsqJOfPfcA/A//aQmWRVOHCKER6r3aIDPnYxz4G\nwMCBAwE477zzgCTq5He/a0gt9vzzzzc6b775sWosvFfpc8TDnO9FixYB0Lt3byDJtzR+/HggyRXw\nmc98ptH5QvTQr371KwAeffRR4OCrs7RXYRQ1vQ7CdyLcZu+Tnccq/TuUPd8/OxIujJpn75d9HqkS\nRVEU/05lbw/CdyZEj3gtKS3btm3LuT1XPbW3nH9hlc4gfIaz//8h0iM9Ai4cG64R+T73Yb9c1xC1\nvRDpE+o4u65DRH1obwM8++yzQPLbd/755xv5UyKyv2fh+hVW0wX48MMPAairqwPguOOOi1fobUoh\nnT9HAf8riqILgeXAjvQnU6nUjQWcS5IkSZIkSa2gkM6f04BnD9zvl/Vc++helyTpMPT99ry2LkJe\na394cVsXQZIkSS2k2Z0/qVRqbEsWRFLl+9OfGvLDp08bOv/884EksXO22bNnAzB9+nQANmzYAGSG\nRYaQ5RDmnD0NrL2Efzcl/T1LX+41hAjPm9fQMRGWdg/1dPHFF2dsD7eQJIl+/PHHAZg7dy6Q1JPv\nvaS2FEURNTU1HHnkkQDs3LkTyPxtMllwecie0ppLe7rmpP9fwzU9fJbDVJ9wvQ6LZqRP7enfvz+Q\nJH4OizqEJMDZbav0BNsqPaGNF+p6zZo1AKxatQrInD65ePFiAM4++2wAjj/+eJd6LwE1NTXxdzl8\n/4477jggmeIFyW9gWKBl69atjaaA5uO3WJIkSZIkqYIVMu1Lkg7Lb37zGyBzlHXixIlAsgxpGIH6\n85//DMCsWbOAJKFw0JzRveykae1pRDCX6urqeCQhfYQnjBasXLkSgOXLlwPwxS9+EUhGFoLXXnst\nvv/MM88A8Mc//hFIRpjCCGJ7S75ZiPRpVtmJN4NjjjkGgB49emQ8Th/5Don/3n33XSBJ4J1P+uht\nn2/NPaSyS+Vi3759vP/++4eUrNbfr9JiPTQWrhkhWiBE/HTu3BlIFnAI28NiGgC9evUCksUewrVk\n7dq1ALz11ltA8r4bIVeawsIa4fcqfCbeeOMNIKnf9KXAQ1stRP507NjRhN4loLq6Ov6uhrZaiOTq\n2rVrvF9YJGfAgAEALFmypNmReXb+SJIkSSo5L7y1raRzpUlSObHzR1KrCbll/ud//ifeFpZwDyMV\n2ZEP2ZozEhtGpxy1zZQ+qhNGFiCJAgrRJ926dQOSEcNs6cu3/+UvfwHgqaeeAmDXrl1FLHFlO9go\nzcc//nEAxo0bB8AnP/lJAHr27Alk1l9YznXZsmVA8p0KS7lmv555G9QeZY9qp+cuy/e7Fa4djoir\n1IXf9ZDz8PTTTwfg1FNPBWD48OEAXHbZZfExIbKnvr4eSHKLhDZBiCbNjihRaQttufAbF3IwpkcF\nh2XD+/TpAzREjvk7VxpC+y7UR/huDxo0KN7nwgsvBJLv+2uvvdbsnE22ACVJkiRJkiqYkT+S2lSh\nkSKFRPEY8ZMpPVok/b0Jq9/069cPgM997nNAEmVysPP87W9/A5I5ydmMvmqeEK02dmzDwprTpk0D\nMkd6AF599dVGx4YcDmEk6NxzzwXg5ptvBpI6CnmCDrZSjlRpUqkUu3fvjkdPs0dTm8MR8bYzrHcX\nlqflR1OipqYmvraGa/BJJ50EJNeDSy+9FIBhw4YBSQQ2wH/+538CyfXn5JNPBpI2QYgMCjmAVJpC\npE+I/MjOFRPa2WF1N0jaDYMHD261cqpp6W3lUI8hSqtTp07xcyeeeCIA77//PtCQNzVEATXFzh9J\nrcZ5+5IkSZLU+uz8kaR2KNeqHWGkcPz48UAy+hdWhTjllFOAJMdM9v10YQTCnBn5pUfghBHXqVOn\nAknET5ir/9vf/hZIVr9LH+G54oorgCRqKOR4CCN7IfJHao9SqRR79uyJR8fDdydX/pLwXPhuGrGo\nUpZ+HQjX9IsuugiAL3/5ywAMHDgw45jvf//78f3HH38cSFYMCqsJjRo1CoDNmzcD8PrrrwNex0tV\nqJcQ1RXyMobbsHJriOqGJLdjiA7atGmTOZ1KQHq7MHynQy6u7t27x8+FtneovyOOOKLZ0azm/JEk\nSZIkSapgRv5IalFrnatf8iZMmADAVVddBcC6desA+O///m8A1q9fD8A111wDwLZt2+Jj840EuuJa\nYUKenuycC08//TQAv/nNb4BkVbV0PXr0yLgNK4ONHj0agAULFgCwZs2aFih5+xM+0+mj7tn5ZIIw\nihdGVP0etJ3w3nfs2BHIzH2SvVJS2Nf6UimLoijO59KrVy8AJk6cCDSO+Hn00UcB+PWvfx1vC5Eh\nZ511FpCs8DlkyBAgufY/9thjQHJdUmkJv1PZ+fxGjhwJwJVXXgkkq4hCEhEcVnQ7/vjjc0aEq3Xl\nalOHet24cWO8beHChUDStti0aVOzc6ja+SNJkpRHhxMG0POqqS1ybjvHJUlSa7HzR5Laierq6nhk\nJ+QDAJg0aRIAHTp0AOAnP/kJkOSXCStEhPn/77zzTnxsUyOBjpznFyIQIFlZLczfDqs7hFwLK1eu\nzHueE044AYA+ffoASQRQeNy5c2egYU44FL7CnlQJwm9fGCnNld8ifSVDqdSlf15D1Gj43Q9CtGjI\nJ5d+LQn7hhWfQq65sJJQOGfIl6XSlP2bFnLEhDyAIaJrx44d8TGh7RbafSoNuSJ/3n77bSAz6j60\nH0PU6o4dO+KVv5pizh9JkiRJkqQKZleuJElSHnveXtns6VlhFPWoo45qOPZAXplwu3fvXvp+e14L\nlFKSJOng7PyRpHaipqYmTuz41a9+Nd5+0kknAfDzn/8cSJYVD0tJXnDBBQDU1tYCyfLjkIQRh1DV\nEB7u9ImmpS/LGcJ6Q1Lms88+G0gS/WUv4RnC9CFZvjWE6oe6eOuttwDYsmVLxrl0aML7GqbR5ZrS\nGKbthc6ecExzl2BVy8v125SevBucrqrysH///viaG35jwtSQrVu3AvDggw8CsGjRokbHh/ZAWByg\nrq4u4/lNmzYByRQhEwKXpuwprKH9cNpppwHJZyHcQjK1PEwHV+kIU7pCmy20xdOvS2GfUPeFfDdt\njUiSJEmSJFUwI38kqR0JUSLDhw+Pt7388stAMjL44YcfAjB06FAAPvvZzwLQpUsXAN5444342JA8\n2MiGwqUvNf3kk08CcMYZZwBJ4s2wfO+wYcOAJNn23//938fHnnPOOUASvfDaa68B8PDDDwNJVJGR\nP4cnvL/hNn2kLd+oWxipc8S8dIRorPRR1Oxt2ZE/RgKpFFVXV3PMMccAyTLQL774IpBcx5ctWwYQ\nJ4MNSZwBvvSlLwHwsY99DEiW/Q7XjKVLl2acK1cyWpWO0F648MILgSTxc4h2TF9kIjwXIkfWr19v\nxHaJCO3p7Aig3bt3x/tkR/zt378/o0150PMXraSSJEmSJEkqOUb+SFI7sXv37nhZ8PSlW0OOgBBd\nEpZ2D/PGw3KhTz/9NJCMLEIyahTOZ4RD86W/V2vXrgVg/vz5AJx++ukAnHrqqQBccsklQJJ7Yfz4\n8fGxYeQ35Pj5wx/+AMDChQsbvY4OXfiMp+dNCEI+rHzH5FpWXK2nqqoqHj3NFcWTr36MdFApi6Io\n/u0JkT8hWueZZ54BYMWKFUCS2+/666+Pj7/ooosyzhe+Iy+99BIAzz33XEsVXUUU8vaEiOHQlgtt\ngxAdkt7uC79t69atA+Cxxx5r9lLhajnDfjbwkI9dftWLTe+EkT+SJEmSJEkVzcgfSWpHQvTOmDFj\n4m2f+MQnABg1ahSQjA6FVY2effZZAGb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12cc34fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yz2/ipzbi+1L++PjCF74AwLHHHgsUjy3A5MmTAbjsssuA7DM1r9Jxbv3D2qim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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ee17710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zzjsB8DwPcFFF+/fvT/zEArV+/XoA7r//fiA+snvQoEEArFy5EnARQKmw8vCLFy8GXDlq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\nRNmzaNEiSUExgoIkjtylaoH0dftNFPPKK69ICiU5H330UUlhRT9tR1b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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f755890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate with images from the validation set as input\n",
    "imgs = sess.run(data_dict.valid_img)\n",
    "tensor_values = sess.run(tensors, {data_dict.train_img: imgs})\n",
    "tensor_values = [imgs] + tensor_values\n",
    "\n",
    "# Plot! \n",
    "examples_to_display = 8\n",
    "for b in xrange(F.batch_size):\n",
    "    if examples_to_display == 0:\n",
    "        break\n",
    "        \n",
    "    tensors_to_display = [tv[:, b] for tv in tensor_values]\n",
    "    presence = tensors_to_display[-2]\n",
    "    \n",
    "    # skip inputs that contain no objects and are just black\n",
    "    # comment the following `if` statement if you'd like to see\n",
    "    # reconstructions of empty inputs\n",
    "    if presence.sum() == 0:\n",
    "        continue\n",
    "        \n",
    "    display_seq(*tensors_to_display)\n",
    "    examples_to_display -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
